Methods of linearizing non-linear chirp signals

ABSTRACT

Systems and methods of linearizing a signal of a light detection and ranging (LiDAR) sensor are described herein. A system receives a portion of a non-linear chirp signal. The portion of the non-linear chirp signal is sampled at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal. A profile of the non-linear chirp signal is generated based on the data points. The non-linear chirp signal is linearized based on the profile of the non-linear chirp signal.

FIELD OF THE INVENTION

The present disclosure relates to light detection and ranging (LiDAR)sensors. More particularly, the present disclosure relates to methods oflinearizing non-linear chirp signals associated with LiDAR sensors.

BACKGROUND

A vehicle such as an autonomous or semi-autonomous vehicle can includemyriad of sensors that can provide continuous stream of sensor datacaptured from a surrounding environment of the vehicle. For example, anautonomous or semi-autonomous vehicle can include cameras, lightdetection and ranging (LiDAR) sensors, radars, Global Positioning System(GPS) devices, sonar-based sensors, ultrasonic sensors, accelerometers,gyroscopes, magnetometers, inertial measurement units (IMUs), farinfrared (FIR) sensors, etc. Such sensor data can enable an autonomousvehicle to perform a number of driving functions that would otherwise beperformed by a human operator. These driving functions, for example, caninclude various vehicle navigation tasks such as vehicle accelerationand deceleration, vehicle braking, vehicle lane changing, adaptivecruise control, blind spot detection, rear-end radar for collisionwarning or collision avoidance, park assisting, cross-trafficmonitoring, emergency braking, automated distance control, and the like.

SUMMARY

Systems and methods of linearizing a signal of a light detection andranging (LiDAR) sensor are described herein. A system can receive aportion of a non-linear chirp signal. The portion of the non-linearchirp signal can be sampled at a sampling frequency to generate datapoints corresponding to the portion of the non-linear chirp signal. Aprofile of the non-linear chirp signal can be generated based on thedata points. The non-linear chirp signal can be linearized based on theprofile of the non-linear chirp signal.

In some embodiments, the non-linear chirp signal can be transmitted bythe LiDAR sensor to an environment associated with the LiDAR sensor andthe portion of the non-linear chirp signal can be diverted from thenon-linear chirp signal before the non-linear chirp signal istransmitted.

In some embodiments, the portion of the non-linear chirp signal can bediverted into a fiber optic ending associated with the LiDAR sensor andthe fiber optic ending can reflect the non-linear chirp signal.

In some embodiments, the fiber optic ending can comprise a fiber opticcable terminated by a reflector. The fiber optic cable can have a cablelength corresponding to a maximum detection range of the LiDAR sensor.

In some embodiments, the sampling frequency can be at least twice of afrequency associated with the non-linear chirp signal. The frequencyassociated with the non-linear chirp signal can correspond to a highestfrequency of the non-linear chirp signal.

In some embodiments, the profile of the non-linear chirp signal can begenerated by generating a best fit curve associated with the non-linearchirp signal based on the data points and determining an equation forthe best fit curve.

In some embodiments, the best fit curve associated with the non-linearchirp signal can be generated by applying one or more regressionanalyses to the data points, generating error values associated with theone or more regression analyses, and selecting a regression analysisfrom the one or more regression analyses based on the error values.

In some embodiments, the error values can be determined based onR-square and satisfy a threshold value.

In some embodiments, the one or more regression analyses can include oneor more of: linear regression, polynomial regression, logarithmicregression, or exponential regression.

In some embodiments, the regression analysis can be selected based on aselection of the one or more regression analyses that has a lowest errorvalue.

In some embodiments, the equation for the best fit curve can bedetermined by generating one or more equations corresponding to the oneor more regression analyses and selecting an equation from the one ormore equations, the equation corresponding to the regression analysisthat has a lowest error value.

In some embodiments, the non-linear chirp signal based on the profile ofthe non-linear chirp signal can be linearized by determining a time atwhich the non-linear chirp signal transmitted by the LiDAR sensor isdetected by the LiDAR sensor and substituting the time into a variableassociated with the equation.

These and other features of the apparatus disclosed herein, as well asthe methods of operation and functions of the related elements ofstructure and the combination of parts and economies of manufacture,will become more apparent upon consideration of the followingdescription and the appended claims with reference to the accompanyingdrawings, all of which form a part of this specification, wherein likereference numerals designate corresponding parts in the various figures.It is to be expressly understood, however, that the drawings are forpurposes of illustration and description only and are not intended as adefinition of the limits of the inventions.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present inventions areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the inventions will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of the inventionare utilized, and the accompanying drawings of which:

FIG. 1 illustrates an example LiDAR sensor in accordance with variousembodiments of the present invention.

FIG. 2 illustrates example chirp signal graphs in accordance withvarious embodiments of the present invention.

FIG. 3 illustrates example power spectrum density graphs in accordancewith various embodiments of the present invention.

FIG. 4 illustrates an example signal linearization system in accordancewith various embodiments of the present invention.

FIG. 5 illustrates an example method in accordance with variousembodiments of the present invention.

FIG. 6 illustrates a block diagram of a computer system upon which anyof the embodiments described herein may be implemented.

The figures depict various embodiments of the disclosed apparatus forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

LiDAR sensors used today are based on time of flight principles. In timeof flight principles, time differences between emitted laser pulses andreturned laser pulses can be measured. Ranges (e.g., distances) ofobjects in an environment can be determined based on these timedifferences. For example, time at which a LiDAR sensor emits a laserpulse is known or can be determined. Similarly, time the which the LiDARsensor receives the laser pulse is also known or can be determined. Adistance (e.g., a range) that the laser pulse traveled can be readilycalculated by multiplying speed of light (i.e., speed at which the laserpulse traveled) by the time difference between the time the laser pulsewas emitted and the time the laser pulse was received. Such LiDARsensors have several disadvantages. For example, a LiDAR sensor based ontime of flight principles cannot simultaneously determine ranges andvelocities of objects in an environment—it can only determine ranges ofobjects. Further, laser pulses emitted from the LiDAR sensor can besubjected to interference from other light sources such as sun light,and thereby affecting accuracy of range determination.

LiDAR sensors based on Frequency Modulated Continuous Wave (FMCW)principles or FMCW LiDAR sensors have been developed to address thedisadvantages of LiDAR sensors based on time of flight principles. InFMCW LiDAR sensors, instead of a pulse, a constant varying frequencysignal (e.g., a chirp signal) is emitted and returned. A frequencyoffset between an emitted chirp signal and a returned chirp signal canbe computed and used to determine a range (e.g., a distance) of anobject in an environment. In addition, a velocity of the object can besimultaneously determined by the FMCW LiDAR sensors using doppler effectprinciples. For example, if an object is moving away from a FMCW LiDARsensor, a chirp signal reflected off from the object would have afrequency that is slight lower (e.g., elongated frequency) than thefrequency at which the chirp signal was emitted. On the other hand, ifan object is moving toward a FMCW LiDAR sensor, a chirp signal reflectedoff from the object would have a frequency that is slight higher (e.g.,compressed frequency) than the frequency at which the chirp signal wasemitted. In general, a chirp signal is more immune to interferences thana pulse signal because the chirp signal operates based on frequenciesand thus is less immune to amplitude interference from bright lightsources such as sun light.

One drawback associated with FMCW LiDAR sensors is that a chirp signalrequired for determining a range and velocity of an object needs to be alinear constant varying frequency signal (e.g., a linear chirp signal).That is, frequency of a chirp signal needs to vary linearly with a timeduration (e.g., a pulse duration or a pulse width) of the chirp signal.Such a linear chirp signal is needed to accurately and reliablydetermine a range and velocity of an object. In today's lasertechnology, a laser source capable of producing such a linear chirpsignal can be cost prohibitive to be widely implemented in FMCW LiDARsensors. Most of commercially available laser sources today arenon-linear laser sources that produce non-linear chirp signals, whichcannot be reliably used in FMCW LiDAR sensors to determine a range andvelocity of an object.

A solution rooted in technology, described herein, addresses theproblems discussed above. In various embodiments, a LiDAR sensor such asa FMCW LiDAR sensor can comprise a sensor housing that includes atransparent window. The sensor housing can further include a laserassembly that includes a laser source (e.g., an non-linear laser source)mounted on a rotating platform that can rotate at various rotationalspeeds. The laser source can emit constant varying frequency signals(e.g., chirp signals) through the transparent window to an environmentoutside of the LiDAR sensor. The chirp signals can reflect off fromobjects in the environment and return to the laser assembly through thetransparent window. The laser assembly can include a laser frequencydetector that detects the returned chirp signals. Based on frequencyoffsets between the emitted and the returned chirp signals, ranges andvelocities of objects in the environment can be determined.

In some embodiments, the laser assembly can further include a frequencymodulator that modulates a laser pulse to a constant varying frequencysignal (e.g., a chirp signal). This chirp signal can be outputted to alaser transceiver of the laser assembly through an optical circulator.The laser transceiver can emit the chirp signal to an environment andreceive the chirp signal reflected from an object in the environment. Asdiscussed, a chirp signal can be an non-linear chirp signal when a lasersource used in the LiDAR sensor is an non-linear laser source. In someembodiments, a portion of an non-linear chirp signal can be divertedfrom the optical circulator to a fiber optic ending while the remainingportion of the non-linear chirp signal is outputted to the lasertransceiver to be emitted to the environment. The fiber optic ending, insome embodiments, comprises a fiber optic cable with a first endingoptically coupled to the optical circulator and a second endingoptically terminated to a reflector. The reflector can reflect thediverted portion of the non-linear chirp signal back to the laserfrequency detector through the fiber optic cable and the opticalcirculator. The laser frequency detector can determine frequency dataassociated with the diverted portion of the non-linear chirp signal.Based on the frequency data, a profile (e.g., a curvature) of thenon-linear chirp signal can be constructed. Based on the profile, amathematical equation that characterizes the profile (e.g., a best fitcurve) and one or more parameters associated with the mathematicalequation can be determined or derived. The mathematical equation and theassociated one or more parameters can be used to linearize thenon-linear chirp signal. Once the non-linear chirp signal is linearized,a frequency offset between an emitted non-linear chirp signal and areturned non-linear chirp signal can be computed. Because a range of anobject is directly proportional to a frequency offset between an emittedchirp signal and a returned chirp which reflected off the object, therange of the object can be determined once the frequency offset isknown.

In some embodiments, a length of the fiber optic cable in the fiberoptic ending can be sized based on a maximum detection range of a LiDARsensor. The length of the fiber optic cable can be sized such that thelength is at least greater than or equal to the maximum detection rangeof the LiDAR sensor. This is because a diverted portion of an non-linearchirp signal needs to travel the same distance (e.g., the maximumdetection range) as the undiverted portion of the non-linear chirpsignal would in an environment in order to accurately characterizenon-linearity (e.g., a profile) of the non-linear chirp signal. Theseand other aspects of the invention will be discussed in greater detailin reference to FIG. 1 below.

FIG. 1 illustrates an example LiDAR sensor 100 in accordance withvarious embodiments of the present invention. In some embodiments, theLiDAR sensor 100 can be a frequency modulated continuous wave (FMCW)LiDAR sensor. The LiDAR sensor 100 can include a laser assembly 102, afiber optic ending 104, and a control unit 108 configured to controlvarious components of the laser assembly 102. The LiDAR sensor 100 candetect a target 106 and determine a range (e.g., distance) and velocityof the target 106 in an environment outside of the LiDAR sensor 100.

In some embodiments, the laser assembly 102 can include a frequencymodulator 110, an optical circulator 112, a laser transceiver 114, and alaser frequency detector 116. The frequency modulator 110 can modulate alaser pulse from a laser source (not shown) into a constant varyingfrequency signal (e.g., a chirp signal). The frequency modulator 110 canmodulate the laser pulse into the constant varying frequency signal bymixing the laser pulse with frequencies from a local oscillator. In someembodiments, the local oscillator can be a voltage controlled localoscillator in which frequency outputted by the voltage controlled localoscillator varies in accordance with an input voltage to the voltagecontrolled local oscillator. Depending on the laser source used in thelaser assembly 102, a chirp signal can be either linear or non-linear.For example, if a laser source is an non-linear laser source, a chirpsignal generated by the frequency modulator 110 is non-linear. On theother hand, if a laser source is a linear laser source, a chirp signalgenerated by the frequency modulator 110 is linear.

In some embodiments, the frequency modulator 110 can be opticallycoupled to an input port of the optical circulator 112. In general, anoptical circulator can be a three port optical device designed such thatlight signals (e.g., an non-linear chirp signal) entering an input portexits a first output port. Any light signals reflected (e.g., comingback) in the first output port are directed to a second output portinstead of the input port of the optical circulator. In this way, anyreflected light signals do not interfere with light signals at the inputport of the optical circulator. In some embodiments, a first output ofthe optical circulator 112 can be optically coupled to the lasertransceiver 114 and a second output of the optical circulator 112 can beoptically coupled to the laser frequency detector 116. In suchembodiments, an non-linear (or linear) chirp signal from the frequencymodulator 110 can be outputted to the laser transceiver 114 through theoptical circulator 112 via an optical path of from the input port to thefirst output port of the optical circulator 112. The non-linear chirpsignal is then emitted to the target 106 by the laser transceiver 114(e.g., indicated by a dash line from the laser transceiver 114 to thetarget 106 in FIG. 1 ). The non-linear chirp signal reflected from thetarget 106 can be received by the laser transceiver 114. This returnednon-linear chirp signal is subsequently received by the laser frequencydetector 116 through the optical circulator 112 via an optical pathwayof from the first output to the second output of the optical circulator112. Time at which the returned non-linear chirp signal is received ordetected by the laser frequency detector 116 can be determined by thecontrol unit 108. For example, the control unit 108 can record timestampdata at which a chirp signal is received or detected by the laserfrequency detector 116.

In some embodiments, the fiber optic ending 104 can include a fiberoptic cable 120 and a reflector 122. In various embodiments, the fiberoptic cable 120 can be any suitable fiber optic cable optically matchedto a frequency of the laser source of the laser assembly 102. Forexample, if a laser source outputs a laser light in 500 to 600nanometers of wavelength, a fiber optic cable suitable to carry light inthe 500 to 600 nanometers of wavelength can be used in the fiber opticending 104. As another example, if a laser source outputs a laser lightin 1000 to 1600 nanometers of wavelength, a fiber optic cable suitableto carry light in the 1000 to 1600 nanometers of wavelength can be usedin the fiber optic ending 104. In some embodiments, a first end of thefiber optic cable 120 can be optically coupled to the first output ofthe optical circulator 112 and a second end of the fiber optic cable 120can be optically terminated to the reflector 122. In variousembodiments, the reflector 122 can be a mirror or any other suitablematerial capable of reflecting light carried by the fiber optic cable120.

In some embodiments, a portion of the non-linear chirp signal from thefirst output of the optical circulator 112 can be diverted into thefiber optic cable 120 of the fiber optic ending 104 through an opticalsplitter (not shown), while the remaining portion of the non-linearchirp signal form the first output of the optical circular 112 isoutputted to the laser transceiver 114 to be emitted to the target 106.This diverted portion of the non-linear chirp signal can travel througha length of the fiber optic cable 120 to the reflector 122. Thereflector 122 reflects the diverted portion of the non-linear chirpsignal back to laser assembly 102 and to the laser frequency detector116 through the optical circulator 112. The laser frequency detector 116can determine frequency data associated with the diverted portion of thenon-linear chirp signal. For example, the laser frequency detector 116can include one or more analog-to-digital converters that convertfrequency of a signal detected into frequency data. This frequency data,in some embodiments, can be used to construct a profile of thenon-linear chirp signal. For example, a profile of a chirp signal can beconstructed by plotting frequency data over pulse width (i.e., time) ofthe chirp signal. In some embodiments, a best fit curve can bedetermined based on the profile of the non-linear chirp signal. From thebest fit curve, a mathematical equation and one or more parametersassociated with the mathematical equation can derived. The mathematicalequation and the one or more parameters can be applied to linearize thenon-linear chirp signal so a frequency offset between an emittednon-linear chirp signal and a returned non-linear chirp signal candetermined. A range of the target 106 can be determined based on thefrequency offset. The linearization or correction of a non-linear chirpsignal will be discussed in greater detail in reference to FIG. 2 below.

In some embodiments, a length of the fiber optic cable 120 can varybased on a maximum detection range of the LiDAR sensor 100. Suchvariations in the length of the fiber optic cable 120 are needed toproperly characterize an non-linear chirp signal. For example, a FMCWLiDAR may have a maximum detection range of 300 meters. In this example,the length of the fiber optic cable 120 needs to be at least 300 metersin order to properly characterize various signal characteristicsassociated with an non-linear chirp signal travelling forward and back300 meters. For instance, frequency and amplitude attenuationsassociated with an non-linear chirp signal may vary depending on adistant the non-linear chirp signal travels. Therefore, by having afiber optic cable equaling at least a maximum detection range of a FMCWLiDAR sensors, various signal characteristics of chirp signals can beproperly characterized.

In some embodiments, the fiber optic ending 104 can be modular. Forexample, the fiber optic ending 104 can be fitted with different lengthsof fiber optic cables based on a type of LiDAR sensor. For example, if afirst FMCW LiDAR sensor has a maximum detectable range of 200 meters, afiber optic ending having a fiber optic cable of at least 200 meters canbe used to characterize non-linear chirp signals emitted from the firstFMCW LiDAR sensor. As another example, if a second FMCW LiDAR sensor hasa maximum detectable range of 500 meters, a fiber optic ending having afiber optic cable of at least 500 meters can be used to characterizenon-linear chirp signals emitted from the second FMCW LiDAR sensor. Inthis example, a fiber optic cable of 200 meter would not be ideal incharacterizing the non-linear chirp signals emitted from the second FMCWLiDAR sensor because attenuations of an non-linear chirp signaltravelling forward and back 500 meters may be different than annon-linear chirp signal travelling forward and back 200 meters. As such,a selection of the fiber optic ending 104 to match a maximum detectionrange of the LiDAR sensor 100 is important in characterizing non-linearchirp signals emitted from the LiDAR sensor 100.

In some embodiments, the control unit 108 can be configured for controlvarious components of the laser assembly 102. For example, the controlunit 108 can be configured to change frequencies of the local oscillatorof the frequency modulator 110 or control operations associated with thelaser transceiver 114 and the laser frequency detector 116. For example,the control unit 108 may instruct the laser transceiver 114 to emit andreceive a chirp signal. As another example, the control unit 108 mayinstruct the laser frequency detector 116 to capture frequency data of achirp signal. In some embodiments, the control unit 108 can beconfigured to construct a profile of an non-linear chirp signal based ona portion of an non-linear chirp signal diverted into the fiber opticending 104 and received by the laser frequency detector 116. The controlunit 108 can determine the profile of the non-linear chirp signal basedon frequency data of the portion of the non-linear chirp signalreflected through the fiber optic ending 104 as detected by the laserfrequency detector 116. Based on the frequency data, the profile of thenon-linear chirp signal can be constructed by the control unit 108. Insome embodiments, the control unit 108 can determine a mathematicalequation (e.g., a best fit curve) to model the profile of the non-linearchirp signal. The mathematical equation can be an exponential curve, apolynomial curve, a logarithmic curve, a linear curve, some combinationsof the aforementioned curves, or any other suitable curves, for example.In some embodiments, the control unit 108 can determine one or moreparameters associated with the mathematical equation. The mathematicalequation and the one or more parameters can be used to linearize thenon-linear chirp signal. The linearization of an non-linear chirp signalis discussed in further detail in reference to FIG. 2 below.

FIG. 2 illustrates example chirp signal graphs 200, 220 in accordancewith various embodiments of the present invention. In some embodiments,a linear chirp signal can be represented by the chirp signal graph 200.The chirp signal graph 200 comprises an x-axis and a y-axis. The x-axisrepresents time (e.g., “Time(t)”) while the y-axis represents frequency(e.g., “Freq(f)”). In some embodiments, the linear chirp signal depictedin the chirp signal graph 200 can comprise a laser pulse 202 (e.g., asawtooth pulse) in which frequency 204 of the laser pulse 202 increaseslinearly within a pulse duration 206 of the laser pulse 202 and thelaser pulse 202 has a maximum frequency “F” In general, the laser pulse202 is called a chirp signal because the frequency 204 of the laserpulse 202 resembles that of a bird chirp (i.e., linearly increasingfrequency). As discussed with respect to FIG. 1 above, in someembodiments, the laser pulse 202 can be emitted to a target (e.g., thetarget 106 in FIG. 1 ). The laser pulse 202 reflected from the targetcan be represented by a returned laser pulse 208 in the chirp signalgraph 200. The returned laser pulse 208 can be detected or captured by alaser frequency detector (e.g., the laser frequency detector 116 in FIG.1 ) at time “T.” As depicted in the chirp graph 200, the laser pulse 202and the returned laser pulse 208 can have a frequency offset 210. Thisfrequency offset 210 can be constant and proportional to a range (e.g.,distance) of the target. The frequency offset 210 can be readilycalculated when a chirp signal is linear. For example, the laser pulse202 can be approximated or modeled as a linear curve (e.g., a best fitcurve) with a mathematical equation f(x)=mx+b, where m is a slope, b isa y-intercept, and x is a variable representing time. In this example,the y-intercept is zero, therefore, the frequency offset 210 can bedetermined by substituting T into x—i.e., the frequency offset 210 canbe computed by multiplying m by T. Because the laser pulse 202 is alinear chirp signal, the slope m of the laser pulse 202 can becalculated by simply dividing F by the pulse duration 206 (e.g., riseover run). As such, a range of a target can be easily determined byusing a linear chirp signal because a frequency offset between anemitted chirp signal and a returned chirp signal can be readily computedbased on a maximum frequency of the emitted chirp signal and time atwhich the returned chirp signal is detected.

In some embodiments, an non-linear chirp signal can be represented bythe chirp signal graph 220. Similar to the chirp graph 200, the chirpsignal graph 220 comprises an x-axis and a y-axis. The x-axis representstime (e.g., “Time(t)”) while the y-axis represents frequency (e.g.,“Freq(f)”). In some embodiments, the non-linear chirp signal depicted inthe chirp signal graph 220 can comprise a laser pulse 222 in whichfrequency 224 of the laser pulse 222 does not increase linearly within apulse duration 226 of the laser pulse 222 and the laser pulse 222 has amaximum frequency “F.” As discussed with respect to FIG. 1 above, insome embodiments, the laser pulse 222 can be emitted to a target (e.g.,the target 106 in FIG. 1 ). The laser pulse 222 reflected from thetarget can be represented by a returned laser pulse 228 in the chirpsignal graph 220. The returned laser pulse 228 can be detected orcaptured by a laser frequency detector (e.g., the laser frequencydetector 116 in FIG. 1 ) at time “T.” However, unlike the case with thelinear chirp signal discussed above, because a profile (e.g., acurvature) of the laser pulse 222 is non-linear, a frequency offset 230between the laser pulse 222 and the returned laser pulse 228 cannot bereadily determined based on a slope of the laser pulse 222. As such, arange of the target cannot be reliably or readily calculated. Therefore,to determine the range of the target, the laser pulse 222 needs to belinearized.

One such linearization technique involves generating a reference chirpsignal associated with an non-linear chirp signal. As used here andelsewhere in this document, “linearize” and/or “linearization” refer toa process of determining a best fit curve (e.g., a mathematicalequation) and determining one or more parameters (e.g., values, numbers,etc.) associated with the best fit curve for an non-linear chirp signal.In some embodiments, a reference chirp signal can be a signal divertedfrom an non-linear chirp signal. This diverted signal can be used tocharacterize a profile (e.g., curvature) of the non-linear chirp signal.Referring back to FIG. 2 , the profile of the laser pulse 222 candetermined based on a signal diverted from the laser pulse 222 (e.g.,the diverted portion of the non-linear chirp signal travelling throughthe fiber optic ending 104 discussed in reference to FIG. 1 ). Thefrequency offset 230 can be determined based on the profile of the laserpulse 222. For example, the profile of the laser pulse 222 can beapproximated or modeled as a logarithmic curve (e.g., a best fit curve)with a mathematical equation f(x)=log₂(x+1)+b, where b is a y-intercept,and x is a variable representing time. In this example, the y-interceptis zero, therefore, the frequency offset 230 can be determined bysubstituting T into x—i.e., the frequency offset 230 can be computed bylog₂(T+1). Accordingly, the range of the target thus can be determinedbased on the frequency offset 230 because the range of the target isproportional to the frequency offset 230.

Now referring back to FIG. 1 , a portion of an non-linear chirp signalfrom frequency modulator 110 can be diverted into the fiber optic ending104 through the optical circulator 112. This diverted portion of thenon-linear chirp signal can travel through the length of the fiber opticcable 120 and reflect, through the reflector 122, back to the laserfrequency detector 116. The control unit 108 can determine a profile ofthe non-linear chirp signal based on frequency data of the divertedportion of the non-linear chirp signal as detected or observed by thelaser frequency detector 112. The control unit 108 can construct aprofile associated with the non-linear chirp signal based on thefrequency data of the diverted portion of the non-linear chirp signal.Based on this profile, the control unit 108 can determine a best fitcurve and one or more parameters for the best fit curve for thenon-linear chirp signal such that a frequency offset between an emittednon-linear chirp signal and a received non-linear chirp can be computedand a range of the target 106 can be correspondingly determined based onthe frequency offset.

In some embodiments, non-linear chirp signals emitted by the laserassembly 102 to the target 106 may vary from one chirp signal to a nextchirp signal. For example, a first non-linear chirp signal emitted bythe laser assembly 102 may have a profile (e.g., a curvature) that isdifferent from a second non-linear chirp signal emitted by the laserassembly 102. For example, the first non-linear chirp signal may have aparabolic or exponential profile and the second non-linear chirp signalmay have a logarithmic profile. As such, to properly correct fornon-linearity of non-linear chirp signals, each non-linear chirp signalneeds to be observed, characterized, and ultimately linearized on asignal-by-signal basis.

FIG. 3 illustrates example power spectrum density graphs 300, 320 inaccordance with various embodiments of the present invention. The powerspectrum density graphs 300, 320 each comprises an x-axis and a y-axis.The x-axis represents frequency components of a chirp signal (e.g.,“Freq(f)”) while the y-axis represents signal power of the chirp signal(e.g., “Power Spectrum Density (Watt/Hz)”). In some embodiments, a chirpsignal can be transformed from time domain to frequency domain byapplying Fourier transformation and represent the chirp signal in thefrequency domain. The power spectrum density graph 300 shows a powerspectrum 302 corresponding to a frequency offset between an emittedlinear chirp signal and a returned linear chirp signal. Because thisfrequency offset is constant for linear chirp signals, the powerspectrum 302 thus has a single peak. In contrast, the power spectrumdensity graph 320 shows a power spectrum 322 corresponding to afrequency offset between an emitted non-linear chirp signal and areturned non-linear chirp signal. Unlike the frequency offset for linearchirp signal, here, because the frequency offset is not constant, thepower spectrum 322 has multiple peaks.

FIG. 4 illustrates an example signal linearization system 400 inaccordance with various embodiments of the present invention. The signallinearization system 400 can include a chirp signal linearization engine402 that can further include one or more processors and memory. The oneor more processors, in conjunction with the memory, can be configured toperform various operations associated with the chirp signallinearization engine 402. For example, the one or more processors andthe memory can be configured to detect frequency of an non-linear chirpsignal to determine a profile (e.g., a curvature) of the non-linearchirp signal. As another example, the one or more processors and thememory can be configured to determine a best fit curve for the profileof the non-linear chirp signal and determine one or more parametersassociated with the best fit curve. As shown in FIG. 4 , the chirpsignal linearization engine 402 can include a non-linearitycharacterization engine 404 and a linearization engine 406.

In some embodiments, the signal linearization system 400 canadditionally include at least one data store 420 that is accessible tothe chirp signal linearization engine 402. In some embodiments, the datastore 420 can be configured to store parameters, data, configurationfiles, or machine-readable codes of the non-linearity characterizationengine 404 and the linearization engine 406.

In various embodiments, the chirp signal linearization engine 402 can beconfigured to characterize non-linearity (e.g., a profile) of a chirpsignal such that the chirp signal can be linearized. As used here andelsewhere in this document, “linearize” and/or “linearization” refer toa process of determining a best fit curve (e.g., a mathematicalequation) and determining one or more parameters (i.e., values, numbers,etc.) associated with the best fit curve for a chirp signal. Further, asused here and elsewhere in this document, “characterize” and“characterization” refer to a process of determining a profile (e.g., acurvature) of an non-linear chirp signal.

In some embodiments, the non-linearity characterization engine 404 canbe configured to characterize non-linearity (e.g., a profile) of anon-linear chirp signal. The non-linearity characterization engine 404can characterize the non-linearity based on a reference signal. Thereference signal, in some embodiments, can be a portion of thenon-linear chirp signal. In some embodiments, the portion of thenon-linear chirp signal can be diverted from the non-linear chirpsignal. For example, one percent of a laser chirp signal from a FMCWLiDAR sensor can be diverted to a laser frequency detector through anoptical splitter and a fiber optic ending. The remaining ninety ninepercent of the laser chirp signal can be emitted by the FMCW LiDARsensor to detect objects in an environment. In some embodiments, thenon-linearity characterization engine 404 can sample the portion of thenon-linear chirp signal at a particular sampling frequency or samplingrate. For example, the non-linearity characterization engine 404 cansample the portion of the non-linear chirp signal at 1 Hz, or one datasample per second. As another example, the non-linearitycharacterization engine 404 can sample the portion of the non-linearchirp signal at 1 kHz, or one thousand data samples per second. Manyvariations are possible. In general, to properly characterize a profileof a signal, a sampling frequency should be set to a least twice of ahighest frequency in the signal. For example, a highest frequencycomponent in a non-linear chirp signal is 500 Hz. In this example, to beable to generate a profile for the non-linear chirp signal, a samplingfrequency should be at least twice of 500 Hz, or 1 kHz.

In some embodiments, the non-linearity characterization engine 404 cangenerate a profile of a non-linear chirp signal. The profile of thenon-linear chirp signal can be generated by determining a best fit curveassociated with the non-linear chirp signal. The best fit curve can bedetermined based on data points sampled by the non-linearitycharacterization engine 404. In some embodiments, the non-linearitycharacterization engine 404 can generate an equation for the best fitcurve. In some embodiments, the non-linearity characterization engine404 can generate the equation for the best fit curve for the non-linearchirp signal by applying one or more regression analyses to the datapoints. The one or more regression analyses can include one or more oflinear regression, polynomial regression, logarithmic regression, orexponential regression. When linear regression is applied to the datapoints, the non-linearity characterization engine 404 can output alinear equation that best fits the data points. When polynomialregression is applied to the data points, the non-linearitycharacterization engine 404 can output a polynomial equation that bestfits the data points. When logarithmic regression is applied to the datapoints, the non-linearity characterization engine 404 outputs alogarithmic equation that best fit the data points. When exponentialregression is applied to the data points, the non-linearitycharacterization engine 404 can output an exponential equation that bestfits the data points. Although only four regression analyses aredescribed above, in some embodiments, the non-linearity characterizationengine 404 can apply other regression analyses to the data points. Theseother regression analyses can include logistic regression, stepwiseregression, ridge regression, lasso regression, for example.

In some embodiments, the non-linearity characterization engine 404 cangenerate error values corresponding to one or more regression applied todata samples. For example, the non-linearity characterization engine 404can generate an error value associated with applying linear regressionto data samples. As another example, the non-linearity characterizationengine 404 can generate an error value associated with applyingpolynomial regression to data samples. In general, an error valueassociated with a regression analysis can indicate a measure of how wellan equation for the regression analysis “fits” data samples to which theregression analysis is applied. In some embodiments, the error value canbe implemented using a R-square method. For example, a R-square of 1.0or 100% can indicate a perfect curve fit between the equation and thedata samples. Whereas, a R-square of 0.5 or 50% indicates a 50% fitbetween the equation and the data samples. In this example, a highR-square value indicates a better curve fit between the equation and thedata samples. In some embodiments, the error value can be implementedusing a standard error instead of the R-square method. Many variationsare possible.

In some embodiments, the non-linearity characterization engine 404 canselect a regression analysis from one or more regression analyses basedon their respective error values. Once selected, the non-linearitycharacterization engine 404 can determine an equation associated withthe selected regression analysis. In some embodiments, the non-linearitycharacterization engine 404 can select a regression analysis that has aleast error. For example, between a first regression analysis with aR-square of 0.95 (or 95%) and a second regression analysis with aR-square of 0.80 (or 80%), the first regression analysis provides abetter curve fit and thus has a least error between the two regressionanalyses. Therefore, in this example, the non-linearity characterizationengine 404 may select the first regression analysis over the secondregression analysis for determining curve fit to data samples. In someembodiments, the non-linearity characterization engine 404 can select aregression analysis from one or more regression analyses based on theirrespective error values satisfying a threshold value. For example, thethreshold value can be 0.75 or 75%. In this example, the non-linearitycharacterization engine 404 can select a regression analysis that has aR-square of at least 0.75 or greater. Many variations are possible.

In some embodiments, the non-linearity characterization engine 404 candetermine one or more parameters associated with an equation of a bestfit curve. The one or more parameters can be numerical scaling factors,constants, and other representations associated with the equation of thebest fit curve. For example, in a case of a best fit curve being alinear equation, one or more parameters associated with the linearequation can be a slope and a y-intercept associated with the linearequation.

In some embodiments, the linearization engine 406 can be configured tolinearize a non-linear chirp signal. The linearization engine 406 canlinearize the non-linear chirp signal based on a profile of thenon-linear chirp signal. The profile of the non-linear chirp signal, asdiscussed above, can be a best fit curve associated with the non-linearchirp signal expressed by an equation. In some embodiments, thelinearization engine 406 can determine a time at which a non-linearsignal transmitted is detected or received. For example, thelinearization engine 406 can determine a time (e.g., timestamp) at whichan emitted non-linear chirp signal is received by a FMCW LiDAR sensor.Once the timestamp is determined, the linearization engine 406 candetermine a frequency offset between the emitted non-linear chirp signaland the received non-linear chirp signal by substituting the timestampinto a variable associated with the equation of the best fit curve andapplying the one or more parameters associated with the best fit curveas determined by the non-linearity characterization engine 404.

FIG. 5 illustrates an example method 500 in accordance with variousembodiments of the present invention. It should be appreciated thatthere can be additional, fewer, or alternative steps performed insimilar or alternative orders, or in parallel, within the scope of thevarious embodiments unless otherwise stated.

At block 502, a processor can receive a portion of a non-linear chirpsignal. At block 504, a processor can sample the portion of thenon-linear chirp signal at a sampling frequency to generate data pointscorresponding to the portion of the non-linear chirp signal. At block506, a processor can generate a profile of the non-linear chirp signalbased on the data points. At block 508, a processor can linearize thenon-linear chirp signal based on the profile of the non-linear chirpsignal.

Hardware Implementation

The techniques described herein are implemented by one or morespecial-purpose computing devices. The special-purpose computing devicesmay be hard-wired to perform the techniques, or may include circuitry ordigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or mayinclude one or more hardware processors programmed to perform thetechniques pursuant to program instructions in firmware, memory, otherstorage, or a combination. Such special-purpose computing devices mayalso combine custom hard-wired logic, ASICs, or FPGAs with customprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, server computer systems,portable computer systems, handheld devices, networking devices or anyother device or combination of devices that incorporate hard-wiredand/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix,Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatibleoperating systems. In other embodiments, the computing device may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface functionality, such as a graphical userinterface (“GUI”), among other things.

FIG. 6 is a block diagram that illustrates a computer system 600 uponwhich any of the embodiments described herein may be implemented. Thecomputer system 600 includes a bus 602 or other communication mechanismfor communicating information, one or more hardware processors 604coupled with bus 602 for processing information. Hardware processor(s)604 may be, for example, one or more general purpose microprocessors.

The computer system 600 also includes a main memory 606, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 602 for storing information and instructions to beexecuted by processor 604. Main memory 606 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 604. Such instructions, whenstored in storage media accessible to processor 604, render computersystem 600 into a special-purpose machine that is customized to performthe operations specified in the instructions.

The computer system 600 further includes a read only memory (ROM) 608 orother static storage device coupled to bus 602 for storing staticinformation and instructions for processor 604. A storage device 610,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 602 for storing information andinstructions.

The computer system 600 may be coupled via bus 602 to a display 612,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 614,including alphanumeric and other keys, is coupled to bus 602 forcommunicating information and command selections to processor 604.Another type of user input device is cursor control 616, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 604 and for controllingcursor movement on display 612. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 600 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 600 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 600 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 600 in response to processor(s) 604 executing one ormore sequences of one or more instructions contained in main memory 606.Such instructions may be read into main memory 606 from another storagemedium, such as storage device 610. Execution of the sequences ofinstructions contained in main memory 606 causes processor(s) 604 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device610. Volatile media includes dynamic memory, such as main memory 606.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 602. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 604 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 602. Bus 602 carries the data tomain memory 606, from which processor 604 retrieves and executes theinstructions. The instructions received by main memory 606 may retrievesand executes the instructions. The instructions received by main memory606 may optionally be stored on storage device 610 either before orafter execution by processor 604.

The computer system 600 also includes a communication interface 618coupled to bus 602. Communication interface 618 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, communication interface 618may be an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 618 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicated with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 618sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 618, which carry the digital data to and fromcomputer system 600, are example forms of transmission media.

The computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link and communicationinterface 618. In the Internet example, a server might transmit arequested code for an application program through the Internet, the ISP,the local network and the communication interface 618.

The received code may be executed by processor 604 as it is received,and/or stored in storage device 610, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

Engines, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, engines, or mechanisms. Engines may constitute eithersoftware engines (e.g., code embodied on a machine-readable medium) orhardware engines. A “hardware engine” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware engines ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware engine may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware enginemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwareengines become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented engine” refers to a hardware engine. Consideringembodiments in which hardware engines are temporarily configured (e.g.,programmed), each of the hardware engines need not be configured orinstantiated at any one instance in time. For example, where a hardwareengine comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware engine at one instance oftime and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented engine” refers to ahardware engine implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

It will be appreciated that an “engine,” “system,” “data store,” and/or“database” may comprise software, hardware, firmware, and/or circuitry.In one example, one or more software programs comprising instructionscapable of being executable by a processor may perform one or more ofthe functions of the engines, data stores, databases, or systemsdescribed herein. In another example, circuitry may perform the same orsimilar functions. Alternative embodiments may comprise more, less, orfunctionally equivalent engines, systems, data stores, or databases, andstill be within the scope of present embodiments. For example, thefunctionality of the various systems, engines, data stores, and/ordatabases may be combined or divided differently.

“Open source” software is defined herein to be source code that allowsdistribution as source code as well as compiled form, with awell-publicized and indexed means of obtaining the source, optionallywith a license that allows modifications and derived works.

The data stores described herein may be any suitable structure (e.g., anactive database, a relational database, a self-referential database, atable, a matrix, an array, a flat file, a documented-oriented storagesystem, a non-relational No-SQL system, and the like), and may becloud-based or otherwise.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, engines, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred implementations, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present invention contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

The invention claimed is:
 1. A computer-implemented method for linearizing a signal of a light detection and ranging (LiDAR) sensor, the method comprising: receiving a portion of a non-linear chirp signal; sampling the portion of the non-linear chirp signal at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal; generate a profile of the non-linear chirp signal based on the data points; and linearizing the non-linear chirp signal based on the profile of the non-linear chirp signal.
 2. The computer-implemented method of claim 1, wherein the sampling frequency is at least twice of a frequency associated with the non-linear chirp signal, and wherein the frequency associated with the non-linear chirp signal corresponds to a highest frequency of the non-linear chirp signal.
 3. The computer-implemented method of claim 1, wherein generating the profile of the non-linear chirp signal based on the data points comprises: generating a best fit curve associated with the non-linear chirp signal based on the data points; and determining an equation for the best fit curve.
 4. The computer-implemented method of claim 3, wherein generating the best fit curve associated with the non-linear chirp signal comprise: applying one or more regression analyses to the data points; generating error values associated with the one or more regression analyses; and selecting a regression analysis from the one or more regression analyses based on the error values.
 5. The computer-implemented method of claim 4, wherein the error values are determined based on R-square and satisfy a threshold value.
 6. The computer-implemented method of claim 5, wherein the regression analysis is selected based on a selection of the one or more regression analyses that has least error.
 7. The computer-implemented method of claim 4, wherein the one or more regression analyses include one or more of: linear regression, polynomial regression, logarithmic regression, or exponential regression.
 8. The computer-implemented method of claim 3, wherein determining the equation for the best fit curve comprise: generating one or more equations corresponding to the one or more regression analyses; and selecting an equation from the one or more equations, the equation corresponding to the regression analysis that has least error.
 9. The computer-implemented method of claim 3, wherein linearizing the non-linear chirp signal based on the profile of the non-linear chirp signal comprises: determining a time at which the non-linear chirp signal transmitted by the LiDAR sensor is detected by the LiDAR sensor; and substituting the time into a variable associated with the equation.
 10. A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving a portion of a non-linear chirp signal; sampling the portion of the non-linear chirp signal at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal; generating a profile of the non-linear chirp signal based on the data points; and linearizing the non-linear chirp signal based on the profile of the non-linear chirp signal.
 11. The system of claim 10, wherein determining the profile of the non-linear chirp signal based on the data points comprises: generating a best fit curve associated with the non-linear chirp signal based on the data points; and determining an equation for the best fit curve.
 12. The system of claim 11, wherein generating the best fit curve associated with the non-linear chirp signal comprise: applying one or more regression analyses to the data points; generating error values associated with the one or more regression analyses; and selecting a regression analysis from the one or more regression analyses based on the error values.
 13. The system of claim 11, wherein determining the equation for the best fit curve comprise: generating one or more equations corresponding to the one or more regression analyses; and selecting an equation from the one or more equations, the equation corresponding to the regression analysis that has least error.
 14. A non-transitory memory storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform a method comprising: receiving a portion of a non-linear chirp signal; sampling the portion of the non-linear chirp signal at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal; generating a profile of the non-linear chirp signal based on the data points; and linearizing the non-linear chirp signal based on the profile of the non-linear chirp signal.
 15. The non-transitory memory of claim 14, wherein generating the profile of the non-linear chirp signal based on the data points comprises: generating a best fit curve associated with the non-linear chirp signal based on the data points; and determining an equation for the best fit curve.
 16. The non-transitory memory of claim 15, wherein generating the best fit curve associated with the non-linear chirp signal comprise: applying one or more regression analyses to the data points; generating error values associated with the one or more regression analyses; and selecting a regression analysis from the one or more regression analyses based on the error values.
 17. The non-transitory memory of claim 15, wherein determining the equation for the best fit curve comprise: generating one or more equations corresponding to the one or more regression analyses; and selecting an equation from the one or more equations, the equation corresponding to the regression analysis that has a lowest error value. 